Implementation and evaluation of the Landsat Ecosystem Disturbance Adaptive Processing Systems (LEDAPS) model: a case study in the Colombian Andes


  • G.M. Valencia Universidad de San Buenaventura
  • J.A. Anaya Universidad de Medellín
  • F.J. Caro-Lopera Universidad de Medellín



LEDAPS, Landsat, radiometric correction, Wilcoxon-Mann-Whitney test, Huber’s method, least trimmed squares, least absolute deviation, bootstrap.


This paper analyzes the reflectance obtained with a series of Landsat images processed with LEDAPS model in a region of the Colombian Andes. A total of 38 images of TM and ETM sensors were calibrated to surface reflectance using LEDAPS in order to determine difference among bands of the same sensor, difference between sensors and analyze temporal patterns. Exact nonparametric statistics allow to conclude that: a) surface reflectance for band 1–5 and 7 were significantly different and this difference remains among images of different dates; b) there are statistical similarities between the TM and ETM sensors bands; c) temporal variations on surface reflectance from the years 1986 to 2013 with the sensors studied are not statistically significant. These results are supported by the implementation of robust modeling with various methods resistant to unusual observations and other typical problems of the classical least squares modeling.


Download data is not yet available.

Author Biographies

G.M. Valencia, Universidad de San Buenaventura

Facultad de Ingenierías

Director Especialización en Sistemas de Información Geográfica

J.A. Anaya, Universidad de Medellín

Facultad de Ingenierías

F.J. Caro-Lopera, Universidad de Medellín

Departamento de Ciencias Básicas


Almeida-Filho, R., Shimabukuro, Y.E. 2002. Digital processing of a Landsat-TM time series for mapping and monitoring degraded areas caused by independent gold miners, Roraima State, Brazilian Amazon. Remote Sensing of Environment, 79(1), 42-50. 4257(01)00237-1

Anaya, J., Colditz, R., Valencia, G. 2015. Land Cover Mapping of a Tropical Region by Integrating Multi-Year Data into an Annual Time Series. Remote Sensing, 7(12), 16274-16292. http://dx.doi. org/10.3390/rs71215833

Anaya, J., Valencia, G. 2013. Fenología de ambientes tropicales en el marco de la teledetección. GeoFocus, 13(2), 195-211.

Byrd, K.B., O’Connell, J.L., Di Tommaso, S., Kelly, M. 2014. Evaluation of sensor types and environmental controls on mapping biomass of coastal marsh emergent vegetation. Remote Sensing of Environment, 149, 166-180. http://dx.doi. org/10.1016/j.rse.2014.04.003

Canty, M.J., Nielsen, A., Schmidt, M. 2004. Automatic radiometric normalization of multitemporal satellite imagery. Remote Sensing of Environment, 91(3-4), 441-451. rse.2003.10.024

Chambers, J., Hastie, T. 1992. Linear models. Statistical Models in S.

Cheng, K.S., Wei, C., Chang, S.C. 2004. Locating landslides using multi-temporal satellite images. Advances in Space Research, 33(3), 296-301. http://

Deschamps, P.Y., Herman, M., Tanre, D. 1983. Modeling of the atmospheric effects and its application to the remote sensing of ocean color. Applied optics, 22(23), 3751-3758. AO.22.003751

Duro, D.C., Girard, J., King, D.J., Fahrig, L., Mitchell, S., Lindsay, K., Tischendorf, L. 2014. Predicting species diversity in agricultural environments using Landsat TM imagery. Remote Sensing of Environment, 144, 214-225. http://dx.doi. org/10.1016/j.rse.2014.01.001

Efron, B., Tibshirani, R.J. 1994. An Introduction to the Bootstrap. (C. Press, Ed.). London.

Ernst, M.D. 2004. Permutation Methods: A Basis for Exact Inference. Statististical0 Science, 19(4), 676-685.

Feng, M., Sexton, J.O., Huang, C., Masek, J.G., Vermote, E.F., Gao, F., Narasimhan, R., Channan, S., Wolfe, R.E., Townshend, J.R. 2013. Global surface reflectance products from Landsat: Assessment using coincident MODIS observations. Remote Sensing of Environment, 134, 276-293. http://dx.doi. org/10.1016/j.rse.2013.02.031

Ganguly, S., Nemani, R.R., Zhang, G., Hashimoto, H., Milesi, C., Michaelis, A., Wang, W., Votava, P., Samanta, A., Melton, F., Dungan, J.L., Vermote, E., Gao, F., Knyazikhin, Y., Myneni, R.B. 2012. Generating global Leaf Area Index from Landsat: Algorithm formulation and demonstration. Remote Sensing of Environment, 122, 185-202. http://dx.doi. org/10.1016/j.rse.2011.10.032

Goodwin, N.R., Collett, L.J. 2014. Development of an automated method for mapping fire history captured in Landsat TM and ETM+ time series across Queensland, Australia. Remote Sensing of Environment, 148, 206-221. http://dx.doi. org/10.1016/j.rse.2014.03.021

Goward, S.N., Masek, J.G. 2001. Landsat - 30 Years and counting. Remote Sensing of Environment, 78(1-2), 1-2. 4257(01)00306-6

Hampel, F.R., Ronchetti, E.M., Rousseeuw, P.J., Stahel, W.A. 1986. Robust Statistics: The Approach Based on Influence Functions. (J. W. y Sons, Ed.). New York.

Hansen, M.C., Loveland, T.R. 2012. A review of large area monitoring of land cover change using Landsat data. Remote Sensing of Environment, 122, 66-74.

Houghton, R. 2012. Carbon emissions and the drivers of deforestation and forest degradation in the tropics. Current Opinion in Environmental Sustainability, 4(6), 597-603. cosust.2012.06.006

Huang, C., Goward, S.N., Masek, J.G., Thomas, N., Zhu, Z., Vogelmann, J. E. 2010. An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sensing of Environment, 114(1), 183-198. http://

Huang, C., Kim, S., Altstatt, A., Townshend, J.R. G., Davis, P., Song, K., Tucker, C.J., Rodas, O., Yanosky, A., Clay, R., Musinsky, J. 2007. Rapid loss of Paraguay’s Atlantic forest and the status of protected areas — A Landsat assessment. Remote Sensing of Environment, 106(4), 460-466. http://

Huang, C., Song, K., Kim, S., Townshend, J.R.G., Davis, P., Masek, J.G., Goward, S.N. 2008. Use of a dark object concept and support vector machines to automate forest cover change analysis. Remote Sensing of Environment, 112(3), 970-985. http://

Huber, P.J., Ronchetti, E.M. 2009. Robust Statistics. (Wiley, Ed.) (2 ed.).

Irons, J.R., Dwyer, J.L., Barsi, J.A. 2012. The next Landsat satellite: The Landsat Data Continuity Mission. Remote Sensing of Environment, 122, 11- 21.

Jong, B., Anaya, C., Masera, O., Olguín, M., Paz, F., Etchevers, J., Martínez, R.D., Guerrero, G., Balbontín, C. 2010. Greenhouse gas emissions between 1993 and 2002 from land-use change and forestry in Mexico. Forest Ecology and Management, 260(10), 1689-1701. http://dx.doi. org/10.1016/j.foreco.2010.08.011

Ju, J., Roy, D.P., Vermote, E.F., Masek, J., Kovalskyy, V. 2012. Continental-scale validation of MODIS-based and LEDAPS Landsat ETM+ atmospheric correction methods. Remote Sensing of Environment, 122, 175- 184.

Kane, V.R., North, M.P., Lutz, J.A., Churchill, D.J., Roberts, S.L., Smith, D.F., McGaughey, R.J., Kane, J.T., Brooks, M.L. 2013. Assessing fire effects on forest spatial structure using a fusion of Landsat and airborne LiDAR data in Yosemite National Park. Remote Sensing of Environment, 151, 89-101. http://

Kaufman, Y.J., Wald, A.E., Remer, L.A., Flynn, L. 1997. The MODIS 2.1-μm channel-correlation with visible reflectance for use in remote sensing of aerosol. IEEE Transactions on Geoscience and Remote Sensing, 35(5), 1286-1298. http://dx.doi. org/10.1109/36.628795

Koenker, R. 1994. Confidence Intervals for Regression Quantiles. In P. Mandl y M. Hušková (Eds.), Asymptotic Statistics (pp. 349-359).

Koenker, R.W. 2005. Quantile Regression. (Cambridge University Press, Ed.).

Lawrence, R.L., Ripple, W.J. 1999. Calculating Change Curves for Multitemporal Satellite Imagery: Mount St . Helens 1980 - 1995, 67(3), 309-319. http://

Levy, R.C., Remer, L., Dubovik, O. 2006. Global aerosol optical models and lookup tables for the new MODIS aerosol retrieval over land. Goddard Space Flight Center. Greenbelt, USA.

Loveland, T.R., Dwyer, J.L. 2012. Landsat: Building a strong future. Remote Sensing of Environment, 122, 22-29.

Lu, D., Mausel, P., Brondizio, E., Moran, E. 2002. Assessment of atmospheric correction methods for Landsat TM data applicable to Amazon basin LBA research. International Journal of Remote Sensing, 23(13), 2651-2671. http://dx.doi. org/10.1080/01431160110109642

Maiersperger, T.K., Scaramuzza, P.L., Leigh, L., Shrestha, S., Gallo, K.P., Jenkerson, C.B., Dwyer, J.L. 2013. Characterizing LEDAPS surface reflectance products by comparisons with AERONET, field spectrometer, and MODIS data. Remote Sensing of Environment, 136, 1-13. rse.2013.04.007

Markham, B.L., Helder, D.L. 2012. Forty-year calibrated record of earth-reflected radiance from Landsat: A review. Remote Sensing of Environment, 122, 30-40.

Masek, J. ., Vermote, E. F., Saleous, N. E., Wolfe, R., Hall, F. G., Huemmrich, K. F., Gao, F., Kutler, J., y Lim, T. 2006. A Landsat Surface Reflectance Dataset for North America, 1990-2000. IEEE Geoscience and Remote Sensing Letters, 3(1), 68-72. http://

Masek, J., Vermote, E.F., Saleous, N., Wolfe, R., Hall, F.G., Huemmrich, F., Gao, F., Kutler, J., Lim, T.K. 2013. LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code. Oak Ridge National Laboratory Distributed Active Archive Center. Tennessee, USA. http://dx.doi. org/10.3334/ORNLDAAC/1080

Miller, H.M., Sexton, N.R., Koontz, L., Loomis, J., Koontz, S.R., Hermans, C. 2011. The users, uses, and value of Landsat and other moderate-resolution satellite imagery in the United States—Executive report: U.S. Geological Survey Open-File Report 2011-1031. U.S. Geological Survey. Virginia. http://

Nazeer, M., Nichol, J.E., Yung, Y.-K. 2014. Evaluation of atmospheric correction models and Landsat surface reflectance product in an urban coastal environment. International Journal of Remote Sensing, 35(16), 6271-6291. 80/01431161.2014.951742

Paolini, L., Grings, F., Sobrino, J.A., Jiménez Muñoz, J. C., Karszenbaum, H. 2006. Radiometric correction effects in Landsat multi-date/multi-sensor change detection studies. International Journal of Remote Sensing, 27(4), 685-704. http://dx.doi. org/10.1080/01431160500183057

Portnoy, S., Koenker, R. 1997. The Gaussian hare and the Laplacian tortoise: computability of squarederror versus absolute-error estimators, 279-300.

Richter, R. 1998. Correction of Satellite Imagery Over Mountainous Terrain. Applied Optics, 37(18), 4004.

Roder, A., Hill, J., Duguy, B., Alloza, J., Vallejo, R. 2008. Using long time series of Landsat data to monitor fire events and post-fire dynamics and identify driving factors. A case study in the Ayora region (eastern Spain). Remote Sensing of Environment, 112(1), 259- 273.

Rousseeuw, P.J., Huber, M. 1997. Recent developments in PROGRESS. In L1-Statistical Procedures and Related Topics. Dodge, IMS Lecture Notes, 31, 201- 214.

Rousseeuw, P.J., Leroy, A.M. 2005. Robust Regression and Outlier Detection. (John Wiley y Sons, Ed.). Wiley.

Roy, D.P., Wulder, M.A., Loveland, T.R., Woodcock, C.E., Allen, R.G., Anderson, M.C., Helder, D., Irons, J.R., Johnson, D.M., Kennedy, R., Scambos, T.A., Schaaf, C.B., Schott, J.R., Sheng, Y., Vermote, E.F., Belward, A.S., Bindschadler, R., … Zhu, Z. 2014. Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 145, 154-172. http://dx.doi. org/10.1016/j.rse.2014.02.001

Rudjord, O., Due Trier, O. 2012. Evaluation of FLAASH atmospheric correction. Oslo.

Schmidt, G., Jenkerson, C., Masek, J., Vermote, E., Gao, F. 2013. Landsat Ecosystem Disturbance Adaptive Processing System ( LEDAPS ) Algorithm Description. Virginia.

Schreier, H., Weber, C. 1999. On the Asymptotic Theory of Permutation Statistics. Mathematical Methods of Statistics, 8, 220-250.

Storey, J., Scaramuzza, P., Schmidt, G., Barsi, J. 2005. Landsat 7 scan line corrector-off gap-filled product development. Global Priorities in Land Remote Sensing, p. 13. South Dakota.

Stroppiana, D., Bordogna, G., Carrara, P., Boschetti, M., Boschetti, L., Brivio, P.A. 2012. A method for extracting burned areas from Landsat TM/ ETM+ images by soft aggregation of multiple Spectral Indices and a region growing algorithm. ISPRS Journal of Photogrammetry and Remote Sensing, 69, 88-102. isprsjprs.2012.03.001

Tan, B., Masek, J.G., Wolfe, R., Gao, F., Huang, C., Vermote, E.F., Sexton, J.O., Ederer, G. 2013. Improved forest change detection with terrain illumination corrected Landsat images. Remote Sensing of Environment, 136, 469-483. http://dx.doi. org/10.1016/j.rse.2013.05.013

Thome, K. 2001. Absolute radiometric calibration of Landsat 7 ETM+ using the reflectance-based method. Remote Sensing of Environment, 78(1-2), 27-38.

Vasconcelos, S.S. De, Fearnside, P.M., Graça, P.M. L.D.A., Nogueira, E.M., Oliveira, L.C. De, y Figueiredo, E.O. 2013. Forest fires in southwestern Brazilian Amazonia: Estimates of area and potential carbon emissions. Forest Ecology and Management, 291, 199-208. foreco.2012.11.044

Vermote, E.F., Saleous, N. 2007. LEDAPS surface reflectance product description. College Park: University of Maryland.

Vermote, E.F., Tanré, D., Deuzé, J.L., Herman, M. 2006. Second Simulation of a Satellite Signal in the Solar Spectrum - Vector ( 6SV ).

Vermote, E.F., Tanré, D., Deuzé, J.L., Herman, M., Morcrette, J.-J. 1997. Second simulation of the satellite signal in the solar spectrum, 6s: an overview. IEEE Transactions on Geoscience and Remote Sensing, 35(3), 675-686. IGARSS.1990.688308

Vicente Serrano, S.M., Pérez-Cabello, F., Lasanta, T. 2008. Assessment of radiometric correction techniques in analyzing vegetation variability and change using time series of Landsat images. Remote Sensing of Environment, 112(10), 3916-3934. http://

Viedma, O., Meliá, J., Segarra, D., García-Haro, J. (1997). Modeling rates of ecosystem recovery after fires by using landsat TM data. Remote Sensing of Environment, 61(3), 383-398. http://dx.doi. org/10.1016/S0034-4257(97)00048-5

Wilkinson, G., Rogers, C.E. 1973. Symbolic Description of Factorial Models for Analysis of Variance. Journal of the Royal Statistical Society. Series C (Applied Statistics), 22(3), 392-399.

Wulder, M.A., Masek, J.G., Cohen, W.B., Loveland, T.R., Woodcock, C.E. 2012. Opening the archive: How free data has enabled the science and monitoring promise of Landsat. Remote Sensing of Environment, 122, 2-10. rse.2012.01.010

Zhu, Z., Woodcock, C.E., Olofsson, P. 2012. Continuous monitoring of forest disturbance using all available Landsat imagery. Remote Sensing of Environment, 122, 75-91. rse.2011.10.030

Zortea, M., Trier Due, O., Salberg, A.-B. 2011. Evaluation of the Landsat surface reflectance estimated by LEDAPS. Oslo.





Research articles